Overview

Brought to you by YData

Dataset statistics

Number of variables36
Number of observations286387
Missing cells0
Missing cells (%)0.0%
Duplicate rows0
Duplicate rows (%)0.0%
Total size in memory78.7 MiB
Average record size in memory288.0 B

Variable types

Numeric12
DateTime2
Categorical10
Text12

Alerts

Discount_Percent is highly overall correlated with discount_amountHigh correlation
Gender is highly overall correlated with Name PrefixHigh correlation
Name Prefix is highly overall correlated with GenderHigh correlation
Region is highly overall correlated with ZipHigh correlation
Zip is highly overall correlated with RegionHigh correlation
bi_st is highly overall correlated with statusHigh correlation
cust_id is highly overall correlated with item_id and 2 other fieldsHigh correlation
discount_amount is highly overall correlated with Discount_PercentHigh correlation
item_id is highly overall correlated with cust_id and 3 other fieldsHigh correlation
month is highly overall correlated with item_id and 2 other fieldsHigh correlation
order_id is highly overall correlated with cust_id and 3 other fieldsHigh correlation
price is highly overall correlated with total and 1 other fieldsHigh correlation
status is highly overall correlated with bi_stHigh correlation
total is highly overall correlated with price and 1 other fieldsHigh correlation
value is highly overall correlated with price and 1 other fieldsHigh correlation
year is highly overall correlated with cust_id and 3 other fieldsHigh correlation
qty_ordered is highly skewed (γ1 = 40.93856178)Skewed
discount_amount is highly skewed (γ1 = 34.31937051)Skewed
item_id has unique valuesUnique
value has 19146 (6.7%) zerosZeros
discount_amount has 200653 (70.1%) zerosZeros
total has 19146 (6.7%) zerosZeros
Discount_Percent has 183081 (63.9%) zerosZeros

Reproduction

Analysis started2024-09-03 18:34:36.495602
Analysis finished2024-09-03 18:36:12.527767
Duration1 minute and 36.03 seconds
Software versionydata-profiling vv4.9.0
Download configurationconfig.json

Variables

order_id
Real number (ℝ)

HIGH CORRELATION 

Distinct201712
Distinct (%)70.4%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean1.0045692 × 108
Minimum1.0035468 × 108
Maximum1.0056239 × 108
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size2.2 MiB
2024-09-03T18:36:12.708010image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/

Quantile statistics

Minimum1.0035468 × 108
5-th percentile1.0036449 × 108
Q11.0040462 × 108
median1.0045177 × 108
Q31.0051341 × 108
95-th percentile1.005529 × 108
Maximum1.0056239 × 108
Range207710
Interquartile range (IQR)108795

Descriptive statistics

Standard deviation60957.857
Coefficient of variation (CV)0.00060680595
Kurtosis-1.2234426
Mean1.0045692 × 108
Median Absolute Deviation (MAD)53989
Skewness0.090082284
Sum2.8769556 × 1013
Variance3.7158604 × 109
MonotonicityNot monotonic
2024-09-03T18:36:13.099142image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
100476608 43
 
< 0.1%
100535753 41
 
< 0.1%
100535744 40
 
< 0.1%
100535749 40
 
< 0.1%
100535739 40
 
< 0.1%
100555234 38
 
< 0.1%
100557806 38
 
< 0.1%
100559787 38
 
< 0.1%
100450381 37
 
< 0.1%
100462724 36
 
< 0.1%
Other values (201702) 285996
99.9%
ValueCountFrequency (%)
100354677 3
< 0.1%
100354678 2
< 0.1%
100354679 2
< 0.1%
100354680 2
< 0.1%
100354681 1
 
< 0.1%
100354682 1
 
< 0.1%
100354683 2
< 0.1%
100354684 1
 
< 0.1%
100354685 1
 
< 0.1%
100354686 1
 
< 0.1%
ValueCountFrequency (%)
100562387 3
< 0.1%
100562386 1
 
< 0.1%
100562385 1
 
< 0.1%
100562384 1
 
< 0.1%
100562383 1
 
< 0.1%
100562382 1
 
< 0.1%
100562381 2
< 0.1%
100562380 1
 
< 0.1%
100562379 1
 
< 0.1%
100562378 1
 
< 0.1%
Distinct365
Distinct (%)0.1%
Missing0
Missing (%)0.0%
Memory size2.2 MiB
Minimum2020-01-10 00:00:00
Maximum2021-12-09 00:00:00
2024-09-03T18:36:13.445330image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-09-03T18:36:13.741610image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
Histogram with fixed size bins (bins=50)

status
Categorical

HIGH CORRELATION 

Distinct13
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size2.2 MiB
canceled
112164 
complete
88968 
received
51775 
order_refunded
25713 
refund
 
3820
Other values (8)
 
3947

Length

Max length14
Median length8
Mean length8.451672
Min length3

Characters and Unicode

Total characters2420449
Distinct characters21
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowreceived
2nd rowreceived
3rd rowcomplete
4th rowcomplete
5th rowreceived

Common Values

ValueCountFrequency (%)
canceled 112164
39.2%
complete 88968
31.1%
received 51775
18.1%
order_refunded 25713
 
9.0%
refund 3820
 
1.3%
cod 2849
 
1.0%
paid 756
 
0.3%
closed 175
 
0.1%
payment_review 57
 
< 0.1%
pending 48
 
< 0.1%
Other values (3) 62
 
< 0.1%

Length

2024-09-03T18:36:14.032957image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
canceled 112164
39.2%
complete 88968
31.1%
received 51775
18.1%
order_refunded 25713
 
9.0%
refund 3820
 
1.3%
cod 2849
 
1.0%
paid 756
 
0.3%
closed 175
 
0.1%
payment_review 57
 
< 0.1%
pending 48
 
< 0.1%
Other values (3) 62
 
< 0.1%

Most occurring characters

ValueCountFrequency (%)
e 639004
26.4%
c 368128
15.2%
d 248780
 
10.3%
l 201336
 
8.3%
n 141891
 
5.9%
r 132824
 
5.5%
o 117763
 
4.9%
a 112985
 
4.7%
p 89874
 
3.7%
m 89025
 
3.7%
Other values (11) 278839
11.5%

Most occurring categories

ValueCountFrequency (%)
(unknown) 2420449
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
e 639004
26.4%
c 368128
15.2%
d 248780
 
10.3%
l 201336
 
8.3%
n 141891
 
5.9%
r 132824
 
5.5%
o 117763
 
4.9%
a 112985
 
4.7%
p 89874
 
3.7%
m 89025
 
3.7%
Other values (11) 278839
11.5%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 2420449
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
e 639004
26.4%
c 368128
15.2%
d 248780
 
10.3%
l 201336
 
8.3%
n 141891
 
5.9%
r 132824
 
5.5%
o 117763
 
4.9%
a 112985
 
4.7%
p 89874
 
3.7%
m 89025
 
3.7%
Other values (11) 278839
11.5%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 2420449
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
e 639004
26.4%
c 368128
15.2%
d 248780
 
10.3%
l 201336
 
8.3%
n 141891
 
5.9%
r 132824
 
5.5%
o 117763
 
4.9%
a 112985
 
4.7%
p 89874
 
3.7%
m 89025
 
3.7%
Other values (11) 278839
11.5%

item_id
Real number (ℝ)

HIGH CORRELATION  UNIQUE 

Distinct286387
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean741662.89
Minimum574769
Maximum905208
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size2.2 MiB
2024-09-03T18:36:14.344489image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/

Quantile statistics

Minimum574769
5-th percentile591361.3
Q1659682
median742305
Q3826121.5
95-th percentile889049.7
Maximum905208
Range330439
Interquartile range (IQR)166439.5

Descriptive statistics

Standard deviation95745.481
Coefficient of variation (CV)0.12909569
Kurtosis-1.2020972
Mean741662.89
Median Absolute Deviation (MAD)83249
Skewness-0.02592401
Sum2.1240261 × 1011
Variance9.1671971 × 109
MonotonicityNot monotonic
2024-09-03T18:36:14.641162image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
574772 1
 
< 0.1%
767992 1
 
< 0.1%
748049 1
 
< 0.1%
748102 1
 
< 0.1%
748048 1
 
< 0.1%
748047 1
 
< 0.1%
861774 1
 
< 0.1%
767993 1
 
< 0.1%
767991 1
 
< 0.1%
769747 1
 
< 0.1%
Other values (286377) 286377
> 99.9%
ValueCountFrequency (%)
574769 1
< 0.1%
574770 1
< 0.1%
574771 1
< 0.1%
574772 1
< 0.1%
574774 1
< 0.1%
574775 1
< 0.1%
574776 1
< 0.1%
574777 1
< 0.1%
574779 1
< 0.1%
574781 1
< 0.1%
ValueCountFrequency (%)
905208 1
< 0.1%
905207 1
< 0.1%
905206 1
< 0.1%
905205 1
< 0.1%
905204 1
< 0.1%
905202 1
< 0.1%
905200 1
< 0.1%
905199 1
< 0.1%
905198 1
< 0.1%
905196 1
< 0.1%

sku
Text

Distinct47932
Distinct (%)16.7%
Missing0
Missing (%)0.0%
Memory size2.2 MiB
2024-09-03T18:36:15.145392image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/

Length

Max length64
Median length19
Mean length19.188413
Min length5

Characters and Unicode

Total characters5495312
Distinct characters75
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique24298 ?
Unique (%)8.5%

Sample

1st rowoasis_Oasis-064-36
2nd rowFantastic_FT-48
3rd rowmdeal_DMC-610-8
4th rowoasis_Oasis-061-36
5th rowMEFNAR59C38B6CA08CD
ValueCountFrequency (%)
matsam59db75adb2f80 3775
 
1.3%
matsam59db757fb47a2 1273
 
0.4%
appnat5a0a01860ce92 1173
 
0.4%
matsam5a7463ee3c1a5 1171
 
0.4%
enteco5a7fe80d6c830 1023
 
0.4%
mattel5a462528e403f 967
 
0.3%
vit5abccf7fdf973 890
 
0.3%
entnob5a4633c950fad 846
 
0.3%
entnob5a14947a21475 771
 
0.3%
matidr59ba510306fbe 732
 
0.3%
Other values (48471) 276182
95.6%
2024-09-03T18:36:16.006537image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
A 649350
 
11.8%
5 461570
 
8.4%
B 312567
 
5.7%
9 294485
 
5.4%
E 290991
 
5.3%
F 286196
 
5.2%
C 262134
 
4.8%
0 232742
 
4.2%
D 230149
 
4.2%
7 211055
 
3.8%
Other values (65) 2264073
41.2%

Most occurring categories

ValueCountFrequency (%)
(unknown) 5495312
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
A 649350
 
11.8%
5 461570
 
8.4%
B 312567
 
5.7%
9 294485
 
5.4%
E 290991
 
5.3%
F 286196
 
5.2%
C 262134
 
4.8%
0 232742
 
4.2%
D 230149
 
4.2%
7 211055
 
3.8%
Other values (65) 2264073
41.2%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 5495312
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
A 649350
 
11.8%
5 461570
 
8.4%
B 312567
 
5.7%
9 294485
 
5.4%
E 290991
 
5.3%
F 286196
 
5.2%
C 262134
 
4.8%
0 232742
 
4.2%
D 230149
 
4.2%
7 211055
 
3.8%
Other values (65) 2264073
41.2%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 5495312
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
A 649350
 
11.8%
5 461570
 
8.4%
B 312567
 
5.7%
9 294485
 
5.4%
E 290991
 
5.3%
F 286196
 
5.2%
C 262134
 
4.8%
0 232742
 
4.2%
D 230149
 
4.2%
7 211055
 
3.8%
Other values (65) 2264073
41.2%

qty_ordered
Real number (ℝ)

SKEWED 

Distinct72
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean3.0113099
Minimum1
Maximum501
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size2.2 MiB
2024-09-03T18:36:16.363052image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/

Quantile statistics

Minimum1
5-th percentile1
Q12
median2
Q33
95-th percentile7
Maximum501
Range500
Interquartile range (IQR)1

Descriptive statistics

Standard deviation4.5738758
Coefficient of variation (CV)1.5188991
Kurtosis3473.5165
Mean3.0113099
Median Absolute Deviation (MAD)0
Skewness40.938562
Sum862400
Variance20.92034
MonotonicityNot monotonic
2024-09-03T18:36:16.671025image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
2 184210
64.3%
3 43684
 
15.3%
1 17572
 
6.1%
6 9960
 
3.5%
4 8457
 
3.0%
11 8457
 
3.0%
5 7015
 
2.4%
21 1572
 
0.5%
7 1538
 
0.5%
9 984
 
0.3%
Other values (62) 2938
 
1.0%
ValueCountFrequency (%)
1 17572
 
6.1%
2 184210
64.3%
3 43684
 
15.3%
4 8457
 
3.0%
5 7015
 
2.4%
6 9960
 
3.5%
7 1538
 
0.5%
8 586
 
0.2%
9 984
 
0.3%
10 595
 
0.2%
ValueCountFrequency (%)
501 5
< 0.1%
381 1
 
< 0.1%
371 1
 
< 0.1%
361 2
 
< 0.1%
351 1
 
< 0.1%
301 2
 
< 0.1%
221 1
 
< 0.1%
211 1
 
< 0.1%
201 4
< 0.1%
188 1
 
< 0.1%

price
Real number (ℝ)

HIGH CORRELATION 

Distinct7561
Distinct (%)2.6%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean851.38756
Minimum0
Maximum101262.59
Zeros1574
Zeros (%)0.5%
Negative0
Negative (%)0.0%
Memory size2.2 MiB
2024-09-03T18:36:16.975607image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile11.9
Q149.9
median119
Q3950
95-th percentile3684.2
Maximum101262.59
Range101262.59
Interquartile range (IQR)900.1

Descriptive statistics

Standard deviation1741.7596
Coefficient of variation (CV)2.0457893
Kurtosis58.618238
Mean851.38756
Median Absolute Deviation (MAD)98
Skewness4.4582822
Sum2.4382633 × 108
Variance3033726.4
MonotonicityNot monotonic
2024-09-03T18:36:17.289497image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
100 7449
 
2.6%
50 7134
 
2.5%
69.9 5250
 
1.8%
99.9 4615
 
1.6%
49.9 4161
 
1.5%
200 3189
 
1.1%
29.9 3110
 
1.1%
1369.8 2743
 
1.0%
1400 2703
 
0.9%
59.9 2645
 
0.9%
Other values (7551) 243388
85.0%
ValueCountFrequency (%)
0 1574
0.5%
0.01 15
 
< 0.1%
0.011 8
 
< 0.1%
0.02 16
 
< 0.1%
0.08 9
 
< 0.1%
0.1 502
 
0.2%
0.13 24
 
< 0.1%
0.15 20
 
< 0.1%
0.16 34
 
< 0.1%
0.2 582
 
0.2%
ValueCountFrequency (%)
101262.59 1
 
< 0.1%
51597.5 1
 
< 0.1%
32000 1
 
< 0.1%
30797 2
 
< 0.1%
29166.7 2
 
< 0.1%
19791.7 1
 
< 0.1%
18969.1 2
 
< 0.1%
18229.2 1
 
< 0.1%
17500 1
 
< 0.1%
16300 91
< 0.1%

value
Real number (ℝ)

HIGH CORRELATION  ZEROS 

Distinct10607
Distinct (%)3.7%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean885.88276
Minimum0
Maximum101262.59
Zeros19146
Zeros (%)6.7%
Negative0
Negative (%)0.0%
Memory size2.2 MiB
2024-09-03T18:36:17.630243image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q149.9
median159
Q3910
95-th percentile3711
Maximum101262.59
Range101262.59
Interquartile range (IQR)860.1

Descriptive statistics

Standard deviation2073.2649
Coefficient of variation (CV)2.3403378
Kurtosis109.37541
Mean885.88276
Median Absolute Deviation (MAD)141
Skewness7.4132032
Sum2.5370531 × 108
Variance4298427.4
MonotonicityNot monotonic
2024-09-03T18:36:17.937984image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0 19146
 
6.7%
69.9 3923
 
1.4%
99.9 3660
 
1.3%
100 3457
 
1.2%
200 3294
 
1.2%
49.9 3240
 
1.1%
250 2949
 
1.0%
500 2872
 
1.0%
400 2404
 
0.8%
1400 2292
 
0.8%
Other values (10597) 239150
83.5%
ValueCountFrequency (%)
0 19146
6.7%
0.01 7
 
< 0.1%
0.011 8
 
< 0.1%
0.02 10
 
< 0.1%
0.04 14
 
< 0.1%
0.08 4
 
< 0.1%
0.1 265
 
0.1%
0.16 5
 
< 0.1%
0.2 327
 
0.1%
0.26 24
 
< 0.1%
ValueCountFrequency (%)
101262.59 1
 
< 0.1%
81500 1
 
< 0.1%
63888.5 1
 
< 0.1%
55500 3
< 0.1%
52900 1
 
< 0.1%
52605 3
< 0.1%
51999.5 2
< 0.1%
49999.5 1
 
< 0.1%
48984 4
< 0.1%
48900 1
 
< 0.1%

discount_amount
Real number (ℝ)

HIGH CORRELATION  SKEWED  ZEROS 

Distinct13732
Distinct (%)4.8%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean70.04065
Minimum0
Maximum30213.15
Zeros200653
Zeros (%)70.1%
Negative0
Negative (%)0.0%
Memory size2.2 MiB
2024-09-03T18:36:18.240526image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median0
Q318.3982
95-th percentile361.04
Maximum30213.15
Range30213.15
Interquartile range (IQR)18.3982

Descriptive statistics

Standard deviation256.88295
Coefficient of variation (CV)3.6676265
Kurtosis3138.1736
Mean70.04065
Median Absolute Deviation (MAD)0
Skewness34.319371
Sum20058732
Variance65988.848
MonotonicityNot monotonic
2024-09-03T18:36:18.554505image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0 200653
70.1%
300 1796
 
0.6%
200 1704
 
0.6%
100 1526
 
0.5%
220 1457
 
0.5%
203 1204
 
0.4%
210.99 939
 
0.3%
323.08 885
 
0.3%
400 689
 
0.2%
230 658
 
0.2%
Other values (13722) 74876
 
26.1%
ValueCountFrequency (%)
0 200653
70.1%
0.01 3
 
< 0.1%
0.02 12
 
< 0.1%
0.03 2
 
< 0.1%
0.04 1
 
< 0.1%
0.1 1
 
< 0.1%
0.192 1
 
< 0.1%
0.218 1
 
< 0.1%
0.258 1
 
< 0.1%
0.27 3
 
< 0.1%
ValueCountFrequency (%)
30213.15 2
< 0.1%
30076.65 2
< 0.1%
20142.1 1
< 0.1%
20051.1 1
< 0.1%
15106.575 2
< 0.1%
15038.325 2
< 0.1%
11918.598 1
< 0.1%
10100 1
< 0.1%
7692.732 1
< 0.1%
7093.584 2
< 0.1%

total
Real number (ℝ)

HIGH CORRELATION  ZEROS 

Distinct23755
Distinct (%)8.3%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean815.84211
Minimum0
Maximum101262.59
Zeros19146
Zeros (%)6.7%
Negative0
Negative (%)0.0%
Memory size2.2 MiB
2024-09-03T18:36:18.876425image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q149.9
median149.8
Q3800
95-th percentile3333
Maximum101262.59
Range101262.59
Interquartile range (IQR)750.1

Descriptive statistics

Standard deviation1983.5921
Coefficient of variation (CV)2.4313431
Kurtosis121.70334
Mean815.84211
Median Absolute Deviation (MAD)132.2
Skewness7.8751239
Sum2.3364657 × 108
Variance3934637.6
MonotonicityNot monotonic
2024-09-03T18:36:19.222760image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0 19146
 
6.7%
69.9 3432
 
1.2%
100 3363
 
1.2%
99.9 3336
 
1.2%
200 3179
 
1.1%
250 2934
 
1.0%
500 2800
 
1.0%
49.9 2738
 
1.0%
400 2302
 
0.8%
29.9 2000
 
0.7%
Other values (23745) 241157
84.2%
ValueCountFrequency (%)
0 19146
6.7%
0.01 7
 
< 0.1%
0.011 8
 
< 0.1%
0.02 10
 
< 0.1%
0.04 14
 
< 0.1%
0.08 4
 
< 0.1%
0.09 3
 
< 0.1%
0.1 262
 
0.1%
0.16 5
 
< 0.1%
0.17 2
 
< 0.1%
ValueCountFrequency (%)
101262.59 1
 
< 0.1%
81500 1
 
< 0.1%
63888.5 1
 
< 0.1%
55500 3
< 0.1%
52900 1
 
< 0.1%
52605 3
< 0.1%
51999.5 2
< 0.1%
49999.5 1
 
< 0.1%
48900 1
 
< 0.1%
47200 1
 
< 0.1%

category
Categorical

Distinct15
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size2.2 MiB
Mobiles & Tablets
61759 
Men's Fashion
40713 
Appliances
33034 
Women's Fashion
28334 
Others
26108 
Other values (10)
96439 

Length

Max length18
Median length17
Mean length12.913551
Min length5

Characters and Unicode

Total characters3698273
Distinct characters37
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowMen's Fashion
2nd rowMen's Fashion
3rd rowMen's Fashion
4th rowMen's Fashion
5th rowMen's Fashion

Common Values

ValueCountFrequency (%)
Mobiles & Tablets 61759
21.6%
Men's Fashion 40713
14.2%
Appliances 33034
11.5%
Women's Fashion 28334
9.9%
Others 26108
9.1%
Beauty & Grooming 17899
 
6.2%
Entertainment 17352
 
6.1%
Superstore 15024
 
5.2%
Home & Living 13990
 
4.9%
Health & Sports 8421
 
2.9%
Other values (5) 23753
 
8.3%

Length

2024-09-03T18:36:19.565245image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
109651
19.1%
fashion 69047
12.0%
mobiles 61759
10.7%
tablets 61759
10.7%
men's 40713
 
7.1%
appliances 33034
 
5.7%
women's 28334
 
4.9%
others 26108
 
4.5%
beauty 17899
 
3.1%
grooming 17899
 
3.1%
Other values (13) 108533
18.9%

Most occurring characters

ValueCountFrequency (%)
e 356769
 
9.6%
s 351505
 
9.5%
288349
 
7.8%
n 264272
 
7.1%
o 252628
 
6.8%
i 242762
 
6.6%
a 229590
 
6.2%
t 206135
 
5.6%
l 166063
 
4.5%
b 130010
 
3.5%
Other values (27) 1210190
32.7%

Most occurring categories

ValueCountFrequency (%)
(unknown) 3698273
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
e 356769
 
9.6%
s 351505
 
9.5%
288349
 
7.8%
n 264272
 
7.1%
o 252628
 
6.8%
i 242762
 
6.6%
a 229590
 
6.2%
t 206135
 
5.6%
l 166063
 
4.5%
b 130010
 
3.5%
Other values (27) 1210190
32.7%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 3698273
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
e 356769
 
9.6%
s 351505
 
9.5%
288349
 
7.8%
n 264272
 
7.1%
o 252628
 
6.8%
i 242762
 
6.6%
a 229590
 
6.2%
t 206135
 
5.6%
l 166063
 
4.5%
b 130010
 
3.5%
Other values (27) 1210190
32.7%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 3698273
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
e 356769
 
9.6%
s 351505
 
9.5%
288349
 
7.8%
n 264272
 
7.1%
o 252628
 
6.8%
i 242762
 
6.6%
a 229590
 
6.2%
t 206135
 
5.6%
l 166063
 
4.5%
b 130010
 
3.5%
Other values (27) 1210190
32.7%

payment_method
Categorical

Distinct13
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size2.2 MiB
cod
102911 
Easypay
69679 
Payaxis
31049 
easypay_voucher
29763 
bankalfalah
23057 
Other values (8)
29928 

Length

Max length17
Median length15
Mean length7.0573979
Min length3

Characters and Unicode

Total characters2021147
Distinct characters30
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique1 ?
Unique (%)< 0.1%

Sample

1st rowcod
2nd rowcod
3rd rowcod
4th rowcod
5th rowcod

Common Values

ValueCountFrequency (%)
cod 102911
35.9%
Easypay 69679
24.3%
Payaxis 31049
 
10.8%
easypay_voucher 29763
 
10.4%
bankalfalah 23057
 
8.1%
Easypay_MA 11536
 
4.0%
jazzwallet 6669
 
2.3%
jazzvoucher 6045
 
2.1%
customercredit 3702
 
1.3%
apg 1758
 
0.6%
Other values (3) 218
 
0.1%

Length

2024-09-03T18:36:19.864606image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
cod 102911
35.9%
easypay 69679
24.3%
payaxis 31049
 
10.8%
easypay_voucher 29763
 
10.4%
bankalfalah 23057
 
8.1%
easypay_ma 11536
 
4.0%
jazzwallet 6669
 
2.3%
jazzvoucher 6045
 
2.1%
customercredit 3702
 
1.3%
apg 1758
 
0.6%
Other values (3) 218
 
0.1%

Most occurring characters

ValueCountFrequency (%)
a 397442
19.7%
y 253005
12.5%
c 146341
 
7.2%
s 145748
 
7.2%
o 142439
 
7.0%
p 112745
 
5.6%
d 106622
 
5.3%
E 81215
 
4.0%
e 79865
 
4.0%
l 59661
 
3.0%
Other values (20) 496064
24.5%

Most occurring categories

ValueCountFrequency (%)
(unknown) 2021147
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
a 397442
19.7%
y 253005
12.5%
c 146341
 
7.2%
s 145748
 
7.2%
o 142439
 
7.0%
p 112745
 
5.6%
d 106622
 
5.3%
E 81215
 
4.0%
e 79865
 
4.0%
l 59661
 
3.0%
Other values (20) 496064
24.5%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 2021147
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
a 397442
19.7%
y 253005
12.5%
c 146341
 
7.2%
s 145748
 
7.2%
o 142439
 
7.0%
p 112745
 
5.6%
d 106622
 
5.3%
E 81215
 
4.0%
e 79865
 
4.0%
l 59661
 
3.0%
Other values (20) 496064
24.5%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 2021147
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
a 397442
19.7%
y 253005
12.5%
c 146341
 
7.2%
s 145748
 
7.2%
o 142439
 
7.0%
p 112745
 
5.6%
d 106622
 
5.3%
E 81215
 
4.0%
e 79865
 
4.0%
l 59661
 
3.0%
Other values (20) 496064
24.5%

bi_st
Categorical

HIGH CORRELATION 

Distinct3
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size2.2 MiB
Gross
112331 
Net
89143 
Valid
84913 

Length

Max length5
Median length5
Mean length4.3774648
Min length3

Characters and Unicode

Total characters1253649
Distinct characters12
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowValid
2nd rowValid
3rd rowNet
4th rowNet
5th rowValid

Common Values

ValueCountFrequency (%)
Gross 112331
39.2%
Net 89143
31.1%
Valid 84913
29.6%

Length

2024-09-03T18:36:20.170259image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-09-03T18:36:20.513917image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
ValueCountFrequency (%)
gross 112331
39.2%
net 89143
31.1%
valid 84913
29.6%

Most occurring characters

ValueCountFrequency (%)
s 224662
17.9%
G 112331
9.0%
r 112331
9.0%
o 112331
9.0%
N 89143
 
7.1%
e 89143
 
7.1%
t 89143
 
7.1%
V 84913
 
6.8%
a 84913
 
6.8%
l 84913
 
6.8%
Other values (2) 169826
13.5%

Most occurring categories

ValueCountFrequency (%)
(unknown) 1253649
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
s 224662
17.9%
G 112331
9.0%
r 112331
9.0%
o 112331
9.0%
N 89143
 
7.1%
e 89143
 
7.1%
t 89143
 
7.1%
V 84913
 
6.8%
a 84913
 
6.8%
l 84913
 
6.8%
Other values (2) 169826
13.5%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 1253649
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
s 224662
17.9%
G 112331
9.0%
r 112331
9.0%
o 112331
9.0%
N 89143
 
7.1%
e 89143
 
7.1%
t 89143
 
7.1%
V 84913
 
6.8%
a 84913
 
6.8%
l 84913
 
6.8%
Other values (2) 169826
13.5%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 1253649
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
s 224662
17.9%
G 112331
9.0%
r 112331
9.0%
o 112331
9.0%
N 89143
 
7.1%
e 89143
 
7.1%
t 89143
 
7.1%
V 84913
 
6.8%
a 84913
 
6.8%
l 84913
 
6.8%
Other values (2) 169826
13.5%

cust_id
Real number (ℝ)

HIGH CORRELATION 

Distinct64248
Distinct (%)22.4%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean70047.608
Minimum4
Maximum115326
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size2.2 MiB
2024-09-03T18:36:20.767115image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/

Quantile statistics

Minimum4
5-th percentile8298
Q156519
median74225
Q392357
95-th percentile110566.7
Maximum115326
Range115322
Interquartile range (IQR)35838

Descriptive statistics

Standard deviation30243.744
Coefficient of variation (CV)0.43175983
Kurtosis-0.31812529
Mean70047.608
Median Absolute Deviation (MAD)17985
Skewness-0.68997414
Sum2.0060724 × 1010
Variance9.1468403 × 108
MonotonicityNot monotonic
2024-09-03T18:36:21.093160image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
85775 2524
 
0.9%
87724 707
 
0.2%
96927 608
 
0.2%
65910 436
 
0.2%
39707 397
 
0.1%
44830 329
 
0.1%
9510 306
 
0.1%
83736 304
 
0.1%
8591 285
 
0.1%
800 277
 
0.1%
Other values (64238) 280214
97.8%
ValueCountFrequency (%)
4 41
 
< 0.1%
15 6
 
< 0.1%
16 20
 
< 0.1%
20 11
 
< 0.1%
21 1
 
< 0.1%
23 6
 
< 0.1%
28 11
 
< 0.1%
32 230
0.1%
33 132
< 0.1%
41 1
 
< 0.1%
ValueCountFrequency (%)
115326 1
 
< 0.1%
115325 2
< 0.1%
115324 1
 
< 0.1%
115323 1
 
< 0.1%
115322 2
< 0.1%
115321 1
 
< 0.1%
115320 3
< 0.1%
115319 3
< 0.1%
115318 1
 
< 0.1%
115317 1
 
< 0.1%

year
Categorical

HIGH CORRELATION 

Distinct2
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size2.2 MiB
2021
177208 
2020
109179 

Length

Max length4
Median length4
Mean length4
Min length4

Characters and Unicode

Total characters1145548
Distinct characters3
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row2020
2nd row2020
3rd row2020
4th row2020
5th row2020

Common Values

ValueCountFrequency (%)
2021 177208
61.9%
2020 109179
38.1%

Length

2024-09-03T18:36:21.410573image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-09-03T18:36:21.736614image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
ValueCountFrequency (%)
2021 177208
61.9%
2020 109179
38.1%

Most occurring characters

ValueCountFrequency (%)
2 572774
50.0%
0 395566
34.5%
1 177208
 
15.5%

Most occurring categories

ValueCountFrequency (%)
(unknown) 1145548
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
2 572774
50.0%
0 395566
34.5%
1 177208
 
15.5%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 1145548
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
2 572774
50.0%
0 395566
34.5%
1 177208
 
15.5%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 1145548
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
2 572774
50.0%
0 395566
34.5%
1 177208
 
15.5%

month
Categorical

HIGH CORRELATION 

Distinct12
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size2.2 MiB
Dec-20
82528 
Apr-21
42137 
Mar-21
26852 
Jun-21
26804 
Nov-20
17364 
Other values (7)
90702 

Length

Max length6
Median length6
Mean length6
Min length6

Characters and Unicode

Total characters1718322
Distinct characters26
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowOct-20
2nd rowOct-20
3rd rowOct-20
4th rowOct-20
5th rowNov-20

Common Values

ValueCountFrequency (%)
Dec-20 82528
28.8%
Apr-21 42137
14.7%
Mar-21 26852
 
9.4%
Jun-21 26804
 
9.4%
Nov-20 17364
 
6.1%
Jan-21 17212
 
6.0%
Jul-21 15339
 
5.4%
May-21 14815
 
5.2%
Sep-21 12483
 
4.4%
Aug-21 11425
 
4.0%
Other values (2) 19428
 
6.8%

Length

2024-09-03T18:36:22.131080image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
dec-20 82528
28.8%
apr-21 42137
14.7%
mar-21 26852
 
9.4%
jun-21 26804
 
9.4%
nov-20 17364
 
6.1%
jan-21 17212
 
6.0%
jul-21 15339
 
5.4%
may-21 14815
 
5.2%
sep-21 12483
 
4.4%
aug-21 11425
 
4.0%
Other values (2) 19428
 
6.8%

Most occurring characters

ValueCountFrequency (%)
- 286387
16.7%
2 286387
16.7%
1 177208
10.3%
0 109179
 
6.4%
e 105152
 
6.1%
c 91815
 
5.3%
D 82528
 
4.8%
r 68989
 
4.0%
J 59355
 
3.5%
a 58879
 
3.4%
Other values (16) 392443
22.8%

Most occurring categories

ValueCountFrequency (%)
(unknown) 1718322
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
- 286387
16.7%
2 286387
16.7%
1 177208
10.3%
0 109179
 
6.4%
e 105152
 
6.1%
c 91815
 
5.3%
D 82528
 
4.8%
r 68989
 
4.0%
J 59355
 
3.5%
a 58879
 
3.4%
Other values (16) 392443
22.8%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 1718322
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
- 286387
16.7%
2 286387
16.7%
1 177208
10.3%
0 109179
 
6.4%
e 105152
 
6.1%
c 91815
 
5.3%
D 82528
 
4.8%
r 68989
 
4.0%
J 59355
 
3.5%
a 58879
 
3.4%
Other values (16) 392443
22.8%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 1718322
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
- 286387
16.7%
2 286387
16.7%
1 177208
10.3%
0 109179
 
6.4%
e 105152
 
6.1%
c 91815
 
5.3%
D 82528
 
4.8%
r 68989
 
4.0%
J 59355
 
3.5%
a 58879
 
3.4%
Other values (16) 392443
22.8%

ref_num
Real number (ℝ)

Distinct62065
Distinct (%)21.7%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean560849.53
Minimum111127
Maximum999981
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size2.2 MiB
2024-09-03T18:36:22.606083image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/

Quantile statistics

Minimum111127
5-th percentile156579
Q1341265
median564853
Q3781082
95-th percentile956935
Maximum999981
Range888854
Interquartile range (IQR)439817

Descriptive statistics

Standard deviation255827.12
Coefficient of variation (CV)0.45614218
Kurtosis-1.1871734
Mean560849.53
Median Absolute Deviation (MAD)220007
Skewness-0.029020885
Sum1.6062001 × 1011
Variance6.5447517 × 1010
MonotonicityNot monotonic
2024-09-03T18:36:23.143974image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
647006 2524
 
0.9%
293634 707
 
0.2%
195394 608
 
0.2%
755020 436
 
0.2%
964852 397
 
0.1%
805628 329
 
0.1%
499153 306
 
0.1%
374032 304
 
0.1%
905286 285
 
0.1%
677450 277
 
0.1%
Other values (62055) 280214
97.8%
ValueCountFrequency (%)
111127 1
 
< 0.1%
111143 1
 
< 0.1%
111197 17
< 0.1%
111213 3
 
< 0.1%
111226 2
 
< 0.1%
111228 1
 
< 0.1%
111239 6
 
< 0.1%
111241 4
 
< 0.1%
111246 2
 
< 0.1%
111258 5
 
< 0.1%
ValueCountFrequency (%)
999981 2
 
< 0.1%
999979 1
 
< 0.1%
999975 3
 
< 0.1%
999973 2
 
< 0.1%
999958 30
< 0.1%
999916 4
 
< 0.1%
999911 1
 
< 0.1%
999907 6
 
< 0.1%
999883 1
 
< 0.1%
999854 6
 
< 0.1%

Name Prefix
Categorical

HIGH CORRELATION 

Distinct7
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size2.2 MiB
Mr.
103503 
Ms.
60313 
Mrs.
47246 
Hon.
30439 
Drs.
16180 
Other values (2)
28706 

Length

Max length5
Median length3
Mean length3.4320832
Min length3

Characters and Unicode

Total characters982904
Distinct characters10
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowDrs.
2nd rowDrs.
3rd rowDrs.
4th rowDrs.
5th rowDrs.

Common Values

ValueCountFrequency (%)
Mr. 103503
36.1%
Ms. 60313
21.1%
Mrs. 47246
16.5%
Hon. 30439
 
10.6%
Drs. 16180
 
5.6%
Prof. 14939
 
5.2%
Dr. 13767
 
4.8%

Length

2024-09-03T18:36:23.709561image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-09-03T18:36:24.237387image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
ValueCountFrequency (%)
mr 103503
36.1%
ms 60313
21.1%
mrs 47246
16.5%
hon 30439
 
10.6%
drs 16180
 
5.6%
prof 14939
 
5.2%
dr 13767
 
4.8%

Most occurring characters

ValueCountFrequency (%)
. 286387
29.1%
M 211062
21.5%
r 195635
19.9%
s 123739
12.6%
o 45378
 
4.6%
H 30439
 
3.1%
n 30439
 
3.1%
D 29947
 
3.0%
P 14939
 
1.5%
f 14939
 
1.5%

Most occurring categories

ValueCountFrequency (%)
(unknown) 982904
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
. 286387
29.1%
M 211062
21.5%
r 195635
19.9%
s 123739
12.6%
o 45378
 
4.6%
H 30439
 
3.1%
n 30439
 
3.1%
D 29947
 
3.0%
P 14939
 
1.5%
f 14939
 
1.5%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 982904
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
. 286387
29.1%
M 211062
21.5%
r 195635
19.9%
s 123739
12.6%
o 45378
 
4.6%
H 30439
 
3.1%
n 30439
 
3.1%
D 29947
 
3.0%
P 14939
 
1.5%
f 14939
 
1.5%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 982904
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
. 286387
29.1%
M 211062
21.5%
r 195635
19.9%
s 123739
12.6%
o 45378
 
4.6%
H 30439
 
3.1%
n 30439
 
3.1%
D 29947
 
3.0%
P 14939
 
1.5%
f 14939
 
1.5%
Distinct5161
Distinct (%)1.8%
Missing0
Missing (%)0.0%
Memory size2.2 MiB
2024-09-03T18:36:24.996225image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/

Length

Max length11
Median length9
Mean length5.8555661
Min length2

Characters and Unicode

Total characters1676958
Distinct characters52
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique7 ?
Unique (%)< 0.1%

Sample

1st rowJani
2nd rowJani
3rd rowJani
4th rowJani
5th rowJani
ValueCountFrequency (%)
joel 2895
 
1.0%
eulah 719
 
0.3%
liz 628
 
0.2%
percy 563
 
0.2%
leigh 505
 
0.2%
ned 448
 
0.2%
yevette 424
 
0.1%
kenton 417
 
0.1%
barry 412
 
0.1%
hortencia 408
 
0.1%
Other values (5151) 278968
97.4%
2024-09-03T18:36:26.216854image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
a 194753
 
11.6%
e 192109
 
11.5%
n 139205
 
8.3%
i 125439
 
7.5%
r 120614
 
7.2%
l 109979
 
6.6%
o 94011
 
5.6%
t 62045
 
3.7%
s 50769
 
3.0%
d 44643
 
2.7%
Other values (42) 543391
32.4%

Most occurring categories

ValueCountFrequency (%)
(unknown) 1676958
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
a 194753
 
11.6%
e 192109
 
11.5%
n 139205
 
8.3%
i 125439
 
7.5%
r 120614
 
7.2%
l 109979
 
6.6%
o 94011
 
5.6%
t 62045
 
3.7%
s 50769
 
3.0%
d 44643
 
2.7%
Other values (42) 543391
32.4%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 1676958
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
a 194753
 
11.6%
e 192109
 
11.5%
n 139205
 
8.3%
i 125439
 
7.5%
r 120614
 
7.2%
l 109979
 
6.6%
o 94011
 
5.6%
t 62045
 
3.7%
s 50769
 
3.0%
d 44643
 
2.7%
Other values (42) 543391
32.4%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 1676958
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
a 194753
 
11.6%
e 192109
 
11.5%
n 139205
 
8.3%
i 125439
 
7.5%
r 120614
 
7.2%
l 109979
 
6.6%
o 94011
 
5.6%
t 62045
 
3.7%
s 50769
 
3.0%
d 44643
 
2.7%
Other values (42) 543391
32.4%

Middle Initial
Categorical

Distinct26
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size2.2 MiB
U
 
13135
N
 
11978
C
 
11891
P
 
11861
W
 
11667
Other values (21)
225855 

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters286387
Distinct characters26
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowW
2nd rowW
3rd rowW
4th rowW
5th rowW

Common Values

ValueCountFrequency (%)
U 13135
 
4.6%
N 11978
 
4.2%
C 11891
 
4.2%
P 11861
 
4.1%
W 11667
 
4.1%
Y 11513
 
4.0%
I 11470
 
4.0%
Q 11287
 
3.9%
E 11183
 
3.9%
J 11151
 
3.9%
Other values (16) 169251
59.1%

Length

2024-09-03T18:36:26.534155image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
u 13135
 
4.6%
n 11978
 
4.2%
c 11891
 
4.2%
p 11861
 
4.1%
w 11667
 
4.1%
y 11513
 
4.0%
i 11470
 
4.0%
q 11287
 
3.9%
e 11183
 
3.9%
j 11151
 
3.9%
Other values (16) 169251
59.1%

Most occurring characters

ValueCountFrequency (%)
U 13135
 
4.6%
N 11978
 
4.2%
C 11891
 
4.2%
P 11861
 
4.1%
W 11667
 
4.1%
Y 11513
 
4.0%
I 11470
 
4.0%
Q 11287
 
3.9%
E 11183
 
3.9%
J 11151
 
3.9%
Other values (16) 169251
59.1%

Most occurring categories

ValueCountFrequency (%)
(unknown) 286387
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
U 13135
 
4.6%
N 11978
 
4.2%
C 11891
 
4.2%
P 11861
 
4.1%
W 11667
 
4.1%
Y 11513
 
4.0%
I 11470
 
4.0%
Q 11287
 
3.9%
E 11183
 
3.9%
J 11151
 
3.9%
Other values (16) 169251
59.1%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 286387
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
U 13135
 
4.6%
N 11978
 
4.2%
C 11891
 
4.2%
P 11861
 
4.1%
W 11667
 
4.1%
Y 11513
 
4.0%
I 11470
 
4.0%
Q 11287
 
3.9%
E 11183
 
3.9%
J 11151
 
3.9%
Other values (16) 169251
59.1%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 286387
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
U 13135
 
4.6%
N 11978
 
4.2%
C 11891
 
4.2%
P 11861
 
4.1%
W 11667
 
4.1%
Y 11513
 
4.0%
I 11470
 
4.0%
Q 11287
 
3.9%
E 11183
 
3.9%
J 11151
 
3.9%
Other values (16) 169251
59.1%
Distinct18208
Distinct (%)6.4%
Missing0
Missing (%)0.0%
Memory size2.2 MiB
2024-09-03T18:36:27.017247image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/

Length

Max length13
Median length11
Mean length6.466362
Min length2

Characters and Unicode

Total characters1851882
Distinct characters52
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique878 ?
Unique (%)0.3%

Sample

1st rowTitus
2nd rowTitus
3rd rowTitus
4th rowTitus
5th rowTitus
ValueCountFrequency (%)
gonzalez 2543
 
0.9%
bailes 715
 
0.2%
melo 615
 
0.2%
braddy 454
 
0.2%
beebe 423
 
0.1%
hohn 369
 
0.1%
nally 315
 
0.1%
glines 313
 
0.1%
matthies 300
 
0.1%
jesse 296
 
0.1%
Other values (18198) 280044
97.8%
2024-09-03T18:36:27.863141image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
e 209195
 
11.3%
a 158744
 
8.6%
r 146694
 
7.9%
n 128551
 
6.9%
o 119850
 
6.5%
l 115206
 
6.2%
i 102891
 
5.6%
s 81184
 
4.4%
t 80344
 
4.3%
u 52590
 
2.8%
Other values (42) 656633
35.5%

Most occurring categories

ValueCountFrequency (%)
(unknown) 1851882
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
e 209195
 
11.3%
a 158744
 
8.6%
r 146694
 
7.9%
n 128551
 
6.9%
o 119850
 
6.5%
l 115206
 
6.2%
i 102891
 
5.6%
s 81184
 
4.4%
t 80344
 
4.3%
u 52590
 
2.8%
Other values (42) 656633
35.5%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 1851882
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
e 209195
 
11.3%
a 158744
 
8.6%
r 146694
 
7.9%
n 128551
 
6.9%
o 119850
 
6.5%
l 115206
 
6.2%
i 102891
 
5.6%
s 81184
 
4.4%
t 80344
 
4.3%
u 52590
 
2.8%
Other values (42) 656633
35.5%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 1851882
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
e 209195
 
11.3%
a 158744
 
8.6%
r 146694
 
7.9%
n 128551
 
6.9%
o 119850
 
6.5%
l 115206
 
6.2%
i 102891
 
5.6%
s 81184
 
4.4%
t 80344
 
4.3%
u 52590
 
2.8%
Other values (42) 656633
35.5%

Gender
Categorical

HIGH CORRELATION 

Distinct2
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size2.2 MiB
M
146181 
F
140206 

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters286387
Distinct characters2
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowF
2nd rowF
3rd rowF
4th rowF
5th rowF

Common Values

ValueCountFrequency (%)
M 146181
51.0%
F 140206
49.0%

Length

2024-09-03T18:36:28.199518image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-09-03T18:36:29.160699image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
ValueCountFrequency (%)
m 146181
51.0%
f 140206
49.0%

Most occurring characters

ValueCountFrequency (%)
M 146181
51.0%
F 140206
49.0%

Most occurring categories

ValueCountFrequency (%)
(unknown) 286387
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
M 146181
51.0%
F 140206
49.0%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 286387
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
M 146181
51.0%
F 140206
49.0%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 286387
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
M 146181
51.0%
F 140206
49.0%

age
Real number (ℝ)

Distinct58
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean46.488811
Minimum18
Maximum75
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size2.2 MiB
2024-09-03T18:36:29.423944image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/

Quantile statistics

Minimum18
5-th percentile20
Q132
median47
Q361
95-th percentile72
Maximum75
Range57
Interquartile range (IQR)29

Descriptive statistics

Standard deviation16.673268
Coefficient of variation (CV)0.35865121
Kurtosis-1.1932583
Mean46.488811
Median Absolute Deviation (MAD)14
Skewness-0.0089064468
Sum13313791
Variance277.99787
MonotonicityNot monotonic
2024-09-03T18:36:29.730124image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
50 7293
 
2.5%
39 6376
 
2.2%
52 5974
 
2.1%
36 5895
 
2.1%
64 5754
 
2.0%
18 5708
 
2.0%
40 5510
 
1.9%
25 5423
 
1.9%
69 5399
 
1.9%
31 5397
 
1.9%
Other values (48) 227658
79.5%
ValueCountFrequency (%)
18 5708
2.0%
19 4917
1.7%
20 4383
1.5%
21 5219
1.8%
22 4588
1.6%
23 4193
1.5%
24 5011
1.7%
25 5423
1.9%
26 4073
1.4%
27 5044
1.8%
ValueCountFrequency (%)
75 4685
1.6%
74 4153
1.5%
73 5283
1.8%
72 4734
1.7%
71 4501
1.6%
70 5042
1.8%
69 5399
1.9%
68 4919
1.7%
67 5072
1.8%
66 4680
1.6%
Distinct64212
Distinct (%)22.4%
Missing0
Missing (%)0.0%
Memory size2.2 MiB
2024-09-03T18:36:30.288664image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/

Length

Max length25
Median length23
Mean length14.321928
Min length6

Characters and Unicode

Total characters4101614
Distinct characters54
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique26418 ?
Unique (%)9.2%

Sample

1st rowTitus, Jani
2nd rowTitus, Jani
3rd rowTitus, Jani
4th rowTitus, Jani
5th rowTitus, Jani
ValueCountFrequency (%)
joel 2895
 
0.5%
gonzalez 2543
 
0.4%
eulah 719
 
0.1%
bailes 715
 
0.1%
liz 628
 
0.1%
melo 615
 
0.1%
percy 579
 
0.1%
jesse 534
 
0.1%
leigh 522
 
0.1%
raymond 463
 
0.1%
Other values (22262) 562561
98.2%
2024-09-03T18:36:31.172499image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
e 401304
 
9.8%
a 353497
 
8.6%
, 286387
 
7.0%
286387
 
7.0%
n 267756
 
6.5%
r 267308
 
6.5%
i 228330
 
5.6%
l 225185
 
5.5%
o 213861
 
5.2%
t 142389
 
3.5%
Other values (44) 1429210
34.8%

Most occurring categories

ValueCountFrequency (%)
(unknown) 4101614
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
e 401304
 
9.8%
a 353497
 
8.6%
, 286387
 
7.0%
286387
 
7.0%
n 267756
 
6.5%
r 267308
 
6.5%
i 228330
 
5.6%
l 225185
 
5.5%
o 213861
 
5.2%
t 142389
 
3.5%
Other values (44) 1429210
34.8%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 4101614
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
e 401304
 
9.8%
a 353497
 
8.6%
, 286387
 
7.0%
286387
 
7.0%
n 267756
 
6.5%
r 267308
 
6.5%
i 228330
 
5.6%
l 225185
 
5.5%
o 213861
 
5.2%
t 142389
 
3.5%
Other values (44) 1429210
34.8%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 4101614
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
e 401304
 
9.8%
a 353497
 
8.6%
, 286387
 
7.0%
286387
 
7.0%
n 267756
 
6.5%
r 267308
 
6.5%
i 228330
 
5.6%
l 225185
 
5.5%
o 213861
 
5.2%
t 142389
 
3.5%
Other values (44) 1429210
34.8%

E Mail
Text

Distinct64246
Distinct (%)22.4%
Missing0
Missing (%)0.0%
Memory size2.2 MiB
2024-09-03T18:36:31.691489image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/

Length

Max length37
Median length34
Mean length23.920999
Min length13

Characters and Unicode

Total characters6850663
Distinct characters28
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique26446 ?
Unique (%)9.2%

Sample

1st rowjani.titus@gmail.com
2nd rowjani.titus@gmail.com
3rd rowjani.titus@gmail.com
4th rowjani.titus@gmail.com
5th rowjani.titus@gmail.com
ValueCountFrequency (%)
joel.gonzalez@yahoo.com 2524
 
0.9%
eulah.bailes@gmail.com 707
 
0.2%
liz.melo@ibm.com 608
 
0.2%
percy.braddy@gmail.com 436
 
0.2%
hortencia.beebe@hotmail.com 397
 
0.1%
saul.hohn@bp.com 329
 
0.1%
ned.nally@yahoo.ca 306
 
0.1%
cathrine.glines@yahoo.com 304
 
0.1%
kenton.matthies@yahoo.com 285
 
0.1%
alfonso.jesse@earthlink.net 277
 
0.1%
Other values (64236) 280214
97.8%
2024-09-03T18:36:32.545854image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
o 679383
 
9.9%
a 632237
 
9.2%
. 585761
 
8.6%
m 505369
 
7.4%
e 494990
 
7.2%
l 476328
 
7.0%
c 399092
 
5.8%
i 398935
 
5.8%
n 346094
 
5.1%
r 336584
 
4.9%
Other values (18) 1995890
29.1%

Most occurring categories

ValueCountFrequency (%)
(unknown) 6850663
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
o 679383
 
9.9%
a 632237
 
9.2%
. 585761
 
8.6%
m 505369
 
7.4%
e 494990
 
7.2%
l 476328
 
7.0%
c 399092
 
5.8%
i 398935
 
5.8%
n 346094
 
5.1%
r 336584
 
4.9%
Other values (18) 1995890
29.1%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 6850663
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
o 679383
 
9.9%
a 632237
 
9.2%
. 585761
 
8.6%
m 505369
 
7.4%
e 494990
 
7.2%
l 476328
 
7.0%
c 399092
 
5.8%
i 398935
 
5.8%
n 346094
 
5.1%
r 336584
 
4.9%
Other values (18) 1995890
29.1%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 6850663
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
o 679383
 
9.9%
a 632237
 
9.2%
. 585761
 
8.6%
m 505369
 
7.4%
e 494990
 
7.2%
l 476328
 
7.0%
c 399092
 
5.8%
i 398935
 
5.8%
n 346094
 
5.1%
r 336584
 
4.9%
Other values (18) 1995890
29.1%
Distinct11647
Distinct (%)4.1%
Missing0
Missing (%)0.0%
Memory size2.2 MiB
Minimum1978-04-11 00:00:00
Maximum2017-12-07 00:00:00
2024-09-03T18:36:32.876405image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-09-03T18:36:33.219440image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
Histogram with fixed size bins (bins=50)

SSN
Text

Distinct64146
Distinct (%)22.4%
Missing0
Missing (%)0.0%
Memory size2.2 MiB
2024-09-03T18:36:33.742314image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/

Length

Max length11
Median length11
Mean length11
Min length11

Characters and Unicode

Total characters3150257
Distinct characters11
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique26363 ?
Unique (%)9.2%

Sample

1st row627-31-5251
2nd row627-31-5251
3rd row627-31-5251
4th row627-31-5251
5th row627-31-5251
ValueCountFrequency (%)
668-48-2887 2524
 
0.9%
077-02-1900 707
 
0.2%
633-31-3682 608
 
0.2%
594-99-2455 436
 
0.2%
187-86-0281 397
 
0.1%
691-18-2163 329
 
0.1%
239-99-1980 306
 
0.1%
030-92-3675 304
 
0.1%
761-12-6286 285
 
0.1%
396-33-9515 277
 
0.1%
Other values (64136) 280214
97.8%
2024-09-03T18:36:34.569338image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
- 572774
18.2%
9 316738
10.1%
1 288314
9.2%
2 283805
9.0%
3 256616
8.1%
8 246557
7.8%
0 242632
7.7%
6 238751
7.6%
7 236860
7.5%
5 236744
7.5%

Most occurring categories

ValueCountFrequency (%)
(unknown) 3150257
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
- 572774
18.2%
9 316738
10.1%
1 288314
9.2%
2 283805
9.0%
3 256616
8.1%
8 246557
7.8%
0 242632
7.7%
6 238751
7.6%
7 236860
7.5%
5 236744
7.5%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 3150257
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
- 572774
18.2%
9 316738
10.1%
1 288314
9.2%
2 283805
9.0%
3 256616
8.1%
8 246557
7.8%
0 242632
7.7%
6 238751
7.6%
7 236860
7.5%
5 236744
7.5%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 3150257
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
- 572774
18.2%
9 316738
10.1%
1 288314
9.2%
2 283805
9.0%
3 256616
8.1%
8 246557
7.8%
0 242632
7.7%
6 238751
7.6%
7 236860
7.5%
5 236744
7.5%
Distinct64248
Distinct (%)22.4%
Missing0
Missing (%)0.0%
Memory size2.2 MiB
2024-09-03T18:36:35.093253image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/

Length

Max length12
Median length12
Mean length12
Min length12

Characters and Unicode

Total characters3436644
Distinct characters11
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique26448 ?
Unique (%)9.2%

Sample

1st row405-959-1129
2nd row405-959-1129
3rd row405-959-1129
4th row405-959-1129
5th row405-959-1129
ValueCountFrequency (%)
217-861-7640 2524
 
0.9%
215-203-6895 707
 
0.2%
303-768-6337 608
 
0.2%
210-868-6884 436
 
0.2%
802-964-9945 397
 
0.1%
216-894-7962 329
 
0.1%
218-472-9253 306
 
0.1%
319-875-2625 304
 
0.1%
314-369-1187 285
 
0.1%
216-580-6285 277
 
0.1%
Other values (64238) 280214
97.8%
2024-09-03T18:36:35.835215image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
- 572774
16.7%
2 454231
13.2%
3 303269
8.8%
0 299453
8.7%
1 290440
8.5%
9 266576
7.8%
5 257456
7.5%
6 254489
7.4%
4 251132
7.3%
7 245944
7.2%

Most occurring categories

ValueCountFrequency (%)
(unknown) 3436644
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
- 572774
16.7%
2 454231
13.2%
3 303269
8.8%
0 299453
8.7%
1 290440
8.5%
9 266576
7.8%
5 257456
7.5%
6 254489
7.4%
4 251132
7.3%
7 245944
7.2%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 3436644
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
- 572774
16.7%
2 454231
13.2%
3 303269
8.8%
0 299453
8.7%
1 290440
8.5%
9 266576
7.8%
5 257456
7.5%
6 254489
7.4%
4 251132
7.3%
7 245944
7.2%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 3436644
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
- 572774
16.7%
2 454231
13.2%
3 303269
8.8%
0 299453
8.7%
1 290440
8.5%
9 266576
7.8%
5 257456
7.5%
6 254489
7.4%
4 251132
7.3%
7 245944
7.2%
Distinct15892
Distinct (%)5.5%
Missing0
Missing (%)0.0%
Memory size2.2 MiB
2024-09-03T18:36:36.423181image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/

Length

Max length26
Median length23
Mean length8.5694497
Min length3

Characters and Unicode

Total characters2454179
Distinct characters56
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique2155 ?
Unique (%)0.8%

Sample

1st rowVinson
2nd rowVinson
3rd rowVinson
4th rowVinson
5th rowVinson
ValueCountFrequency (%)
city 7190
 
2.0%
new 4099
 
1.1%
lake 3029
 
0.8%
san 2842
 
0.8%
dekalb 2525
 
0.7%
saint 2301
 
0.6%
west 2282
 
0.6%
washington 2167
 
0.6%
springs 1968
 
0.5%
fort 1795
 
0.5%
Other values (12858) 328917
91.6%
2024-09-03T18:36:37.631410image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
e 228158
 
9.3%
a 207528
 
8.5%
n 185171
 
7.5%
o 182044
 
7.4%
l 174712
 
7.1%
r 153853
 
6.3%
i 152066
 
6.2%
t 130705
 
5.3%
s 100732
 
4.1%
72728
 
3.0%
Other values (46) 866482
35.3%

Most occurring categories

ValueCountFrequency (%)
(unknown) 2454179
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
e 228158
 
9.3%
a 207528
 
8.5%
n 185171
 
7.5%
o 182044
 
7.4%
l 174712
 
7.1%
r 153853
 
6.3%
i 152066
 
6.2%
t 130705
 
5.3%
s 100732
 
4.1%
72728
 
3.0%
Other values (46) 866482
35.3%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 2454179
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
e 228158
 
9.3%
a 207528
 
8.5%
n 185171
 
7.5%
o 182044
 
7.4%
l 174712
 
7.1%
r 153853
 
6.3%
i 152066
 
6.2%
t 130705
 
5.3%
s 100732
 
4.1%
72728
 
3.0%
Other values (46) 866482
35.3%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 2454179
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
e 228158
 
9.3%
a 207528
 
8.5%
n 185171
 
7.5%
o 182044
 
7.4%
l 174712
 
7.1%
r 153853
 
6.3%
i 152066
 
6.2%
t 130705
 
5.3%
s 100732
 
4.1%
72728
 
3.0%
Other values (46) 866482
35.3%

County
Text

Distinct2551
Distinct (%)0.9%
Missing0
Missing (%)0.0%
Memory size2.2 MiB
2024-09-03T18:36:38.438893image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/

Length

Max length22
Median length20
Mean length7.4509143
Min length3

Characters and Unicode

Total characters2133845
Distinct characters58
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique110 ?
Unique (%)< 0.1%

Sample

1st rowHarmon
2nd rowHarmon
3rd rowHarmon
4th rowHarmon
5th rowHarmon
ValueCountFrequency (%)
san 3624
 
1.1%
jefferson 3539
 
1.1%
los 3210
 
1.0%
angeles 3198
 
1.0%
dekalb 3098
 
1.0%
new 3044
 
0.9%
washington 2863
 
0.9%
st 2812
 
0.9%
montgomery 2563
 
0.8%
orange 2474
 
0.8%
Other values (2527) 291927
90.6%
2024-09-03T18:36:39.819636image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
a 209937
 
9.8%
e 206814
 
9.7%
n 169638
 
7.9%
o 161510
 
7.6%
r 144882
 
6.8%
l 117408
 
5.5%
i 113156
 
5.3%
s 97453
 
4.6%
t 90433
 
4.2%
u 52738
 
2.5%
Other values (48) 769876
36.1%

Most occurring categories

ValueCountFrequency (%)
(unknown) 2133845
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
a 209937
 
9.8%
e 206814
 
9.7%
n 169638
 
7.9%
o 161510
 
7.6%
r 144882
 
6.8%
l 117408
 
5.5%
i 113156
 
5.3%
s 97453
 
4.6%
t 90433
 
4.2%
u 52738
 
2.5%
Other values (48) 769876
36.1%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 2133845
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
a 209937
 
9.8%
e 206814
 
9.7%
n 169638
 
7.9%
o 161510
 
7.6%
r 144882
 
6.8%
l 117408
 
5.5%
i 113156
 
5.3%
s 97453
 
4.6%
t 90433
 
4.2%
u 52738
 
2.5%
Other values (48) 769876
36.1%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 2133845
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
a 209937
 
9.8%
e 206814
 
9.7%
n 169638
 
7.9%
o 161510
 
7.6%
r 144882
 
6.8%
l 117408
 
5.5%
i 113156
 
5.3%
s 97453
 
4.6%
t 90433
 
4.2%
u 52738
 
2.5%
Other values (48) 769876
36.1%

City
Text

Distinct15892
Distinct (%)5.5%
Missing0
Missing (%)0.0%
Memory size2.2 MiB
2024-09-03T18:36:40.624553image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/

Length

Max length26
Median length23
Mean length8.5694497
Min length3

Characters and Unicode

Total characters2454179
Distinct characters56
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique2155 ?
Unique (%)0.8%

Sample

1st rowVinson
2nd rowVinson
3rd rowVinson
4th rowVinson
5th rowVinson
ValueCountFrequency (%)
city 7190
 
2.0%
new 4099
 
1.1%
lake 3029
 
0.8%
san 2842
 
0.8%
dekalb 2525
 
0.7%
saint 2301
 
0.6%
west 2282
 
0.6%
washington 2167
 
0.6%
springs 1968
 
0.5%
fort 1795
 
0.5%
Other values (12858) 328917
91.6%
2024-09-03T18:36:41.424410image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
e 228158
 
9.3%
a 207528
 
8.5%
n 185171
 
7.5%
o 182044
 
7.4%
l 174712
 
7.1%
r 153853
 
6.3%
i 152066
 
6.2%
t 130705
 
5.3%
s 100732
 
4.1%
72728
 
3.0%
Other values (46) 866482
35.3%

Most occurring categories

ValueCountFrequency (%)
(unknown) 2454179
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
e 228158
 
9.3%
a 207528
 
8.5%
n 185171
 
7.5%
o 182044
 
7.4%
l 174712
 
7.1%
r 153853
 
6.3%
i 152066
 
6.2%
t 130705
 
5.3%
s 100732
 
4.1%
72728
 
3.0%
Other values (46) 866482
35.3%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 2454179
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
e 228158
 
9.3%
a 207528
 
8.5%
n 185171
 
7.5%
o 182044
 
7.4%
l 174712
 
7.1%
r 153853
 
6.3%
i 152066
 
6.2%
t 130705
 
5.3%
s 100732
 
4.1%
72728
 
3.0%
Other values (46) 866482
35.3%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 2454179
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
e 228158
 
9.3%
a 207528
 
8.5%
n 185171
 
7.5%
o 182044
 
7.4%
l 174712
 
7.1%
r 153853
 
6.3%
i 152066
 
6.2%
t 130705
 
5.3%
s 100732
 
4.1%
72728
 
3.0%
Other values (46) 866482
35.3%

State
Text

Distinct51
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size2.2 MiB
2024-09-03T18:36:41.850376image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/

Length

Max length2
Median length2
Mean length2
Min length2

Characters and Unicode

Total characters572774
Distinct characters24
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowOK
2nd rowOK
3rd rowOK
4th rowOK
5th rowOK
ValueCountFrequency (%)
tx 17510
 
6.1%
ca 17130
 
6.0%
ny 15567
 
5.4%
pa 14394
 
5.0%
il 12628
 
4.4%
fl 10538
 
3.7%
oh 10203
 
3.6%
mo 8876
 
3.1%
va 8557
 
3.0%
ia 8170
 
2.9%
Other values (41) 162814
56.9%
2024-09-03T18:36:42.512162image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
A 84799
14.8%
N 60368
 
10.5%
M 45727
 
8.0%
I 43840
 
7.7%
C 38388
 
6.7%
T 33804
 
5.9%
L 33127
 
5.8%
O 32547
 
5.7%
Y 24691
 
4.3%
K 20038
 
3.5%
Other values (14) 155445
27.1%

Most occurring categories

ValueCountFrequency (%)
(unknown) 572774
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
A 84799
14.8%
N 60368
 
10.5%
M 45727
 
8.0%
I 43840
 
7.7%
C 38388
 
6.7%
T 33804
 
5.9%
L 33127
 
5.8%
O 32547
 
5.7%
Y 24691
 
4.3%
K 20038
 
3.5%
Other values (14) 155445
27.1%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 572774
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
A 84799
14.8%
N 60368
 
10.5%
M 45727
 
8.0%
I 43840
 
7.7%
C 38388
 
6.7%
T 33804
 
5.9%
L 33127
 
5.8%
O 32547
 
5.7%
Y 24691
 
4.3%
K 20038
 
3.5%
Other values (14) 155445
27.1%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 572774
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
A 84799
14.8%
N 60368
 
10.5%
M 45727
 
8.0%
I 43840
 
7.7%
C 38388
 
6.7%
T 33804
 
5.9%
L 33127
 
5.8%
O 32547
 
5.7%
Y 24691
 
4.3%
K 20038
 
3.5%
Other values (14) 155445
27.1%

Zip
Real number (ℝ)

HIGH CORRELATION 

Distinct33773
Distinct (%)11.8%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean49723.44
Minimum210
Maximum99950
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size2.2 MiB
2024-09-03T18:36:42.833069image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/

Quantile statistics

Minimum210
5-th percentile6467
Q126573
median49316
Q372645
95-th percentile95251
Maximum99950
Range99740
Interquartile range (IQR)46072

Descriptive statistics

Standard deviation27597.274
Coefficient of variation (CV)0.55501539
Kurtosis-1.0980033
Mean49723.44
Median Absolute Deviation (MAD)23095
Skewness0.064251513
Sum1.4240147 × 1010
Variance7.6160956 × 108
MonotonicityNot monotonic
2024-09-03T18:36:43.147223image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
60115 2525
 
0.9%
16201 712
 
0.2%
80749 608
 
0.2%
79853 437
 
0.2%
5777 402
 
0.1%
44867 329
 
0.1%
50072 327
 
0.1%
56317 308
 
0.1%
65261 285
 
0.1%
45427 278
 
0.1%
Other values (33763) 280176
97.8%
ValueCountFrequency (%)
210 1
 
< 0.1%
212 4
 
< 0.1%
214 1
 
< 0.1%
215 6
 
< 0.1%
401 3
 
< 0.1%
501 6
 
< 0.1%
544 24
< 0.1%
1001 6
 
< 0.1%
1002 3
 
< 0.1%
1004 1
 
< 0.1%
ValueCountFrequency (%)
99950 1
 
< 0.1%
99929 1
 
< 0.1%
99928 42
< 0.1%
99927 12
 
< 0.1%
99926 4
 
< 0.1%
99922 1
 
< 0.1%
99921 2
 
< 0.1%
99919 13
 
< 0.1%
99903 41
< 0.1%
99901 11
 
< 0.1%

Region
Categorical

HIGH CORRELATION 

Distinct4
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size2.2 MiB
South
103481 
Midwest
81296 
West
51080 
Northeast
50530 

Length

Max length9
Median length7
Mean length6.0951335
Min length4

Characters and Unicode

Total characters1745567
Distinct characters15
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowSouth
2nd rowSouth
3rd rowSouth
4th rowSouth
5th rowSouth

Common Values

ValueCountFrequency (%)
South 103481
36.1%
Midwest 81296
28.4%
West 51080
17.8%
Northeast 50530
17.6%

Length

2024-09-03T18:36:43.440136image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-09-03T18:36:43.740280image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
ValueCountFrequency (%)
south 103481
36.1%
midwest 81296
28.4%
west 51080
17.8%
northeast 50530
17.6%

Most occurring characters

ValueCountFrequency (%)
t 336917
19.3%
e 182906
10.5%
s 182906
10.5%
o 154011
8.8%
h 154011
8.8%
S 103481
 
5.9%
u 103481
 
5.9%
M 81296
 
4.7%
i 81296
 
4.7%
d 81296
 
4.7%
Other values (5) 283966
16.3%

Most occurring categories

ValueCountFrequency (%)
(unknown) 1745567
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
t 336917
19.3%
e 182906
10.5%
s 182906
10.5%
o 154011
8.8%
h 154011
8.8%
S 103481
 
5.9%
u 103481
 
5.9%
M 81296
 
4.7%
i 81296
 
4.7%
d 81296
 
4.7%
Other values (5) 283966
16.3%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 1745567
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
t 336917
19.3%
e 182906
10.5%
s 182906
10.5%
o 154011
8.8%
h 154011
8.8%
S 103481
 
5.9%
u 103481
 
5.9%
M 81296
 
4.7%
i 81296
 
4.7%
d 81296
 
4.7%
Other values (5) 283966
16.3%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 1745567
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
t 336917
19.3%
e 182906
10.5%
s 182906
10.5%
o 154011
8.8%
h 154011
8.8%
S 103481
 
5.9%
u 103481
 
5.9%
M 81296
 
4.7%
i 81296
 
4.7%
d 81296
 
4.7%
Other values (5) 283966
16.3%
Distinct64006
Distinct (%)22.3%
Missing0
Missing (%)0.0%
Memory size2.2 MiB
2024-09-03T18:36:44.216696image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/

Length

Max length15
Median length13
Mean length8.466362
Min length4

Characters and Unicode

Total characters2424656
Distinct characters26
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique26246 ?
Unique (%)9.2%

Sample

1st rowjwtitus
2nd rowjwtitus
3rd rowjwtitus
4th rowjwtitus
5th rowjwtitus
ValueCountFrequency (%)
jugonzalez 2524
 
0.9%
exbailes 707
 
0.2%
lumelo 608
 
0.2%
pnbraddy 436
 
0.2%
hmbeebe 397
 
0.1%
sahohn 329
 
0.1%
nenally 306
 
0.1%
ccglines 304
 
0.1%
knmatthies 285
 
0.1%
agjesse 277
 
0.1%
Other values (63996) 280214
97.8%
2024-09-03T18:36:45.072864image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
e 242475
 
10.0%
a 198597
 
8.2%
r 189167
 
7.8%
l 161461
 
6.7%
n 154592
 
6.4%
s 140605
 
5.8%
o 139109
 
5.7%
i 120962
 
5.0%
t 115455
 
4.8%
c 98826
 
4.1%
Other values (16) 863407
35.6%

Most occurring categories

ValueCountFrequency (%)
(unknown) 2424656
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
e 242475
 
10.0%
a 198597
 
8.2%
r 189167
 
7.8%
l 161461
 
6.7%
n 154592
 
6.4%
s 140605
 
5.8%
o 139109
 
5.7%
i 120962
 
5.0%
t 115455
 
4.8%
c 98826
 
4.1%
Other values (16) 863407
35.6%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 2424656
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
e 242475
 
10.0%
a 198597
 
8.2%
r 189167
 
7.8%
l 161461
 
6.7%
n 154592
 
6.4%
s 140605
 
5.8%
o 139109
 
5.7%
i 120962
 
5.0%
t 115455
 
4.8%
c 98826
 
4.1%
Other values (16) 863407
35.6%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 2424656
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
e 242475
 
10.0%
a 198597
 
8.2%
r 189167
 
7.8%
l 161461
 
6.7%
n 154592
 
6.4%
s 140605
 
5.8%
o 139109
 
5.7%
i 120962
 
5.0%
t 115455
 
4.8%
c 98826
 
4.1%
Other values (16) 863407
35.6%

Discount_Percent
Real number (ℝ)

HIGH CORRELATION  ZEROS 

Distinct17009
Distinct (%)5.9%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean6.0691849
Minimum0
Maximum75
Zeros183081
Zeros (%)63.9%
Negative0
Negative (%)0.0%
Memory size2.2 MiB
2024-09-03T18:36:45.411926image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median0
Q311
95-th percentile23.602301
Maximum75
Range75
Interquartile range (IQR)11

Descriptive statistics

Standard deviation10.100156
Coefficient of variation (CV)1.6641701
Kurtosis6.8569835
Mean6.0691849
Median Absolute Deviation (MAD)0
Skewness2.235571
Sum1738135.7
Variance102.01315
MonotonicityNot monotonic
2024-09-03T18:36:45.727892image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0 183081
63.9%
20 10726
 
3.7%
10 8674
 
3.0%
15 6281
 
2.2%
9 2795
 
1.0%
5 1419
 
0.5%
14.5 1328
 
0.5%
30 1310
 
0.5%
11 1173
 
0.4%
15.40297854 939
 
0.3%
Other values (16999) 68661
 
24.0%
ValueCountFrequency (%)
0 183081
63.9%
0.038464497 1
 
< 0.1%
0.039214724 1
 
< 0.1%
0.066872428 1
 
< 0.1%
0.07518797 2
 
< 0.1%
0.076 1
 
< 0.1%
0.076101469 1
 
< 0.1%
0.076666667 2
 
< 0.1%
0.076842105 1
 
< 0.1%
0.076923077 1
 
< 0.1%
ValueCountFrequency (%)
75 51
< 0.1%
70 4
 
< 0.1%
69.97493734 1
 
< 0.1%
69.97159091 1
 
< 0.1%
69.95833333 1
 
< 0.1%
69.94156928 1
 
< 0.1%
69.93006993 1
 
< 0.1%
69.92307692 1
 
< 0.1%
69.91631799 1
 
< 0.1%
69.85667293 1
 
< 0.1%

Interactions

2024-09-03T18:36:01.986773image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-09-03T18:35:16.750392image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-09-03T18:35:20.606949image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-09-03T18:35:25.169860image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-09-03T18:35:29.766676image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-09-03T18:35:33.300509image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-09-03T18:35:36.822511image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-09-03T18:35:42.268461image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-09-03T18:35:45.752859image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-09-03T18:35:49.382814image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-09-03T18:35:53.697917image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-09-03T18:35:58.407698image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-09-03T18:36:02.286401image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-09-03T18:35:17.030093image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-09-03T18:35:20.928150image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-09-03T18:35:25.534203image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-09-03T18:35:30.071258image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-09-03T18:35:33.586603image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-09-03T18:35:37.125533image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-09-03T18:35:42.555971image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-09-03T18:35:46.077884image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-09-03T18:35:49.638700image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-09-03T18:35:54.173148image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-09-03T18:35:58.699319image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-09-03T18:36:02.586631image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-09-03T18:35:17.332706image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-09-03T18:35:21.277634image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-09-03T18:35:26.055347image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-09-03T18:35:30.377360image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-09-03T18:35:33.873789image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-09-03T18:35:37.529947image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-09-03T18:35:42.845198image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-09-03T18:35:46.369940image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-09-03T18:35:49.925122image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-09-03T18:35:54.636434image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-09-03T18:35:59.006146image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-09-03T18:36:02.900288image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-09-03T18:35:17.633967image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-09-03T18:35:21.575003image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-09-03T18:35:26.535957image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-09-03T18:35:30.665347image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-09-03T18:35:34.166862image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-09-03T18:35:37.997880image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-09-03T18:35:43.143593image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-09-03T18:35:46.652943image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-09-03T18:35:50.229245image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-09-03T18:35:55.048754image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-09-03T18:35:59.304919image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-09-03T18:36:03.201407image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-09-03T18:35:17.928855image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-09-03T18:35:21.898038image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-09-03T18:35:27.030459image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-09-03T18:35:30.949066image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-09-03T18:35:34.462358image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-09-03T18:35:38.342430image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-09-03T18:35:43.437442image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-09-03T18:35:46.950141image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-09-03T18:35:50.503916image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-09-03T18:35:55.474536image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-09-03T18:35:59.592192image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-09-03T18:36:03.495102image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-09-03T18:35:18.230034image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-09-03T18:35:22.223937image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-09-03T18:35:27.400000image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-09-03T18:35:31.262074image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-09-03T18:35:34.742119image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-09-03T18:35:39.262455image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-09-03T18:35:43.712015image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-09-03T18:35:47.265489image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-09-03T18:35:50.780492image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-09-03T18:35:55.896741image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-09-03T18:35:59.870781image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-09-03T18:36:03.811319image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-09-03T18:35:18.527476image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-09-03T18:35:22.530089image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-09-03T18:35:27.685811image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-09-03T18:35:31.543057image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-09-03T18:35:35.066040image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-09-03T18:35:39.649143image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-09-03T18:35:44.018897image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-09-03T18:35:47.569225image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-09-03T18:35:51.446817image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-09-03T18:35:56.389149image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-09-03T18:36:00.181025image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-09-03T18:36:04.102179image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-09-03T18:35:18.821984image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-09-03T18:35:22.844000image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-09-03T18:35:27.963806image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-09-03T18:35:31.817525image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-09-03T18:35:35.362094image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-09-03T18:35:40.078864image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-09-03T18:35:44.304627image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-09-03T18:35:47.857547image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-09-03T18:35:51.725148image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-09-03T18:35:56.808855image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-09-03T18:36:00.461872image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-09-03T18:36:04.411992image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-09-03T18:35:19.135745image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-09-03T18:35:23.277081image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-09-03T18:35:28.292639image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-09-03T18:35:32.120528image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-09-03T18:35:35.651568image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-09-03T18:35:40.461327image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-09-03T18:35:44.579379image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-09-03T18:35:48.187383image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-09-03T18:35:52.031842image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-09-03T18:35:57.247497image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-09-03T18:36:00.758309image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-09-03T18:36:04.697008image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-09-03T18:35:19.677078image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-09-03T18:35:23.724046image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-09-03T18:35:28.574203image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-09-03T18:35:32.409132image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-09-03T18:35:35.938270image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-09-03T18:35:40.905725image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-09-03T18:35:44.867735image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-09-03T18:35:48.470999image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-09-03T18:35:52.366510image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-09-03T18:35:57.529765image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-09-03T18:36:01.086381image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-09-03T18:36:04.995803image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-09-03T18:35:19.980639image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-09-03T18:35:24.189201image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-09-03T18:35:29.193100image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-09-03T18:35:32.680891image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-09-03T18:35:36.234889image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-09-03T18:35:41.400446image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-09-03T18:35:45.199694image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-09-03T18:35:48.747229image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-09-03T18:35:52.780032image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-09-03T18:35:57.809500image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-09-03T18:36:01.384886image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-09-03T18:36:05.291264image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-09-03T18:35:20.288440image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-09-03T18:35:24.642673image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-09-03T18:35:29.476710image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-09-03T18:35:32.971810image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-09-03T18:35:36.527144image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-09-03T18:35:41.844156image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-09-03T18:35:45.472296image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-09-03T18:35:49.073153image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-09-03T18:35:53.236037image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-09-03T18:35:58.113299image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-09-03T18:36:01.683750image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/

Correlations

2024-09-03T18:36:46.028449image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
Discount_PercentGenderMiddle InitialName PrefixRegionZipagebi_stcategorycust_iddiscount_amountitem_idmonthorder_idpayment_methodpriceqty_orderedref_numstatustotalvalueyear
Discount_Percent1.0000.0160.0250.0110.011-0.003-0.0030.1570.155-0.1800.907-0.1900.137-0.1900.2080.3110.085-0.0050.0780.1890.2400.278
Gender0.0161.0000.0440.9460.0310.0430.0290.0180.0560.0390.0060.0180.0230.0200.0200.0030.0020.0480.0200.0080.0100.003
Middle Initial0.0250.0441.0000.0540.0530.0620.0610.0300.0680.0610.0110.0370.0380.0370.0320.0180.0080.0610.0220.0230.0250.051
Name Prefix0.0110.9460.0541.0000.0280.0320.0330.0200.0360.0380.0040.0180.0230.0180.0180.0070.0040.0400.0160.0100.0100.013
Region0.0110.0310.0530.0281.0000.9550.0340.0110.0530.0410.0040.0220.0270.0230.0230.0090.0040.0400.0170.0120.0120.012
Zip-0.0030.0430.0620.0320.9551.0000.0010.0200.0540.002-0.004-0.0070.023-0.0070.020-0.002-0.003-0.0160.016-0.002-0.0030.025
age-0.0030.0290.0610.0330.0340.0011.0000.0200.054-0.009-0.005-0.0070.028-0.0070.024-0.0100.009-0.0130.017-0.006-0.0060.035
bi_st0.1570.0180.0300.0200.0110.0200.0201.0000.2170.2020.0110.2940.2880.2990.4010.0560.0050.0221.0000.0580.0580.192
category0.1550.0560.0680.0360.0530.0540.0540.2171.0000.2020.0130.1800.1750.1880.1540.0650.0130.0530.1010.0540.0540.241
cust_id-0.1800.0390.0610.0380.0410.002-0.0090.2020.2021.000-0.1570.5260.3890.5260.1800.1160.004-0.0140.1060.1130.1010.666
discount_amount0.9070.0060.0110.0040.004-0.004-0.0050.0110.013-0.1571.000-0.1940.015-0.1940.0210.3410.173-0.0050.0090.4300.4730.009
item_id-0.1900.0180.0370.0180.022-0.007-0.0070.2940.1800.526-0.1941.0000.7581.0000.2450.0060.0740.0090.1640.0330.0270.979
month0.1370.0230.0380.0230.0270.0230.0280.2880.1750.3890.0150.7581.0000.7330.2380.0590.0140.0220.1500.0530.0531.000
order_id-0.1900.0200.0370.0180.023-0.007-0.0070.2990.1880.526-0.1941.0000.7331.0000.2490.0060.0740.0090.1650.0330.0270.944
payment_method0.2080.0200.0320.0180.0230.0200.0240.4010.1540.1800.0210.2450.2380.2491.0000.0780.0000.0220.1740.0660.0670.356
price0.3110.0030.0180.0070.009-0.002-0.0100.0560.0650.1160.3410.0060.0590.0060.0781.000-0.302-0.0130.0360.6870.6830.055
qty_ordered0.0850.0020.0080.0040.004-0.0030.0090.0050.0130.0040.1730.0740.0140.0740.000-0.3021.0000.0060.0130.2950.3130.009
ref_num-0.0050.0480.0610.0400.040-0.016-0.0130.0220.053-0.014-0.0050.0090.0220.0090.022-0.0130.0061.0000.015-0.008-0.0080.024
status0.0780.0200.0220.0160.0170.0160.0171.0000.1010.1060.0090.1640.1500.1650.1740.0360.0130.0151.0000.0300.0300.236
total0.1890.0080.0230.0100.012-0.002-0.0060.0580.0540.1130.4300.0330.0530.0330.0660.6870.295-0.0080.0301.0000.9970.057
value0.2400.0100.0250.0100.012-0.003-0.0060.0580.0540.1010.4730.0270.0530.0270.0670.6830.313-0.0080.0300.9971.0000.058
year0.2780.0030.0510.0130.0120.0250.0350.1920.2410.6660.0090.9791.0000.9440.3560.0550.0090.0240.2360.0570.0581.000

Missing values

2024-09-03T18:36:06.112044image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
A simple visualization of nullity by column.
2024-09-03T18:36:09.388503image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
Nullity matrix is a data-dense display which lets you quickly visually pick out patterns in data completion.

Sample

order_idorder_datestatusitem_idskuqty_orderedpricevaluediscount_amounttotalcategorypayment_methodbi_stcust_idyearmonthref_numName PrefixFirst NameMiddle InitialLast NameGenderagefull_nameE MailCustomer SinceSSNPhone No.Place NameCountyCityStateZipRegionUser NameDiscount_Percent
010035467801-10-2020received574772oasis_Oasis-064-362189.91798.00.01798.0Men's FashioncodValid601242020Oct-20987867Drs.JaniWTitusF43Titus, Janijani.titus@gmail.com22-08-2006627-31-5251405-959-1129VinsonHarmonVinsonOK73571Southjwtitus0.0
110035467801-10-2020received574774Fantastic_FT-481119.0190.00.0190.0Men's FashioncodValid601242020Oct-20987867Drs.JaniWTitusF43Titus, Janijani.titus@gmail.com22-08-2006627-31-5251405-959-1129VinsonHarmonVinsonOK73571Southjwtitus0.0
210035468001-10-2020complete574777mdeal_DMC-610-89149.91199.20.01199.2Men's FashioncodNet601242020Oct-20987867Drs.JaniWTitusF43Titus, Janijani.titus@gmail.com22-08-2006627-31-5251405-959-1129VinsonHarmonVinsonOK73571Southjwtitus0.0
310035468001-10-2020complete574779oasis_Oasis-061-36979.9639.20.0639.2Men's FashioncodNet601242020Oct-20987867Drs.JaniWTitusF43Titus, Janijani.titus@gmail.com22-08-2006627-31-5251405-959-1129VinsonHarmonVinsonOK73571Southjwtitus0.0
410036735713-11-2020received595185MEFNAR59C38B6CA08CD299.999.90.099.9Men's FashioncodValid601242020Nov-20987867Drs.JaniWTitusF43Titus, Janijani.titus@gmail.com22-08-2006627-31-5251405-959-1129VinsonHarmonVinsonOK73571Southjwtitus0.0
510036735713-11-2020received595186MEFBUY59B7C3DDC2CA3-42239.939.90.039.9Men's FashioncodValid601242020Nov-20987867Drs.JaniWTitusF43Titus, Janijani.titus@gmail.com22-08-2006627-31-5251405-959-1129VinsonHarmonVinsonOK73571Southjwtitus0.0
610036736013-11-2020order_refunded595192MATDAN59C3C845B38F0247.647.60.047.6Mobiles & TabletscodValid601242020Nov-20987867Drs.JaniWTitusF43Titus, Janijani.titus@gmail.com22-08-2006627-31-5251405-959-1129VinsonHarmonVinsonOK73571Southjwtitus0.0
710035467701-10-2020canceled574769GFE_19_USBLEDLight249.049.00.049.0Mobiles & TabletsPayaxisGross424852020Oct-20171143Prof.LeeSEakerM28Eaker, Leelee.eaker@gmail.com04-02-1981185-86-4345239-335-6755GrahamBradfordGrahamFL32042Southlseaker0.0
810035467701-10-2020canceled574770oasis_Kingston-32GB-DTIG42135.0135.00.0135.0ComputingPayaxisGross424852020Oct-20171143Prof.LeeSEakerM28Eaker, Leelee.eaker@gmail.com04-02-1981185-86-4345239-335-6755GrahamBradfordGrahamFL32042Southlseaker0.0
910035467701-10-2020canceled574771Geepas_GSB54202549.9549.90.0549.9AppliancesPayaxisGross424852020Oct-20171143Prof.LeeSEakerM28Eaker, Leelee.eaker@gmail.com04-02-1981185-86-4345239-335-6755GrahamBradfordGrahamFL32042Southlseaker0.0
order_idorder_datestatusitem_idskuqty_orderedpricevaluediscount_amounttotalcategorypayment_methodbi_stcust_idyearmonthref_numName PrefixFirst NameMiddle InitialLast NameGenderagefull_nameE MailCustomer SinceSSNPhone No.Place NameCountyCityStateZipRegionUser NameDiscount_Percent
28637710056235230-09-2021paid905161BAGKEM5A703890E559C257.257.20.057.2Beauty & GroomingbankalfalahValid236112021Sep-21501027Ms.DanilleOVanF68Van, Danilledanille.van@exxonmobil.com20-09-2012079-02-3985603-914-7130AcworthSullivanAcworthNH3601Northeastdovan0.0
28637810056235530-09-2021canceled905165QAD5B33702879C59299.999.90.099.9SuperstorebankalfalahGross78282021Sep-21517420Mrs.ShaunaGMazzeoF61Mazzeo, Shaunashauna.mazzeo@gmail.com31-08-2013770-02-7961270-661-7834LouisvilleJeffersonLouisvilleKY40293Southsgmazzeo0.0
28637910056236230-09-2021canceled905174MATSAM5B6D7208C6D30212999.912999.90.012999.9Mobiles & TabletsbankalfalahGross1153212021Sep-21670487Mrs.DreamaPWellF45Well, Dreamadreama.well@gmail.com16-12-2003258-99-6039212-413-5652BronxBronxBronxNY10465Northeastdpwell0.0
28638010056236330-09-2021canceled905175MEFMOV5AD9155B0C0DC-M2104.8104.80.0104.8Men's FashionbankalfalahGross1153222021Sep-21639345Ms.ShainaKNowakF56Nowak, Shainashaina.nowak@gmail.com29-11-2009320-11-4545215-735-9443ChambersburgChambersburgChambersburgPA17202Northeastsknowak0.0
28638110056236430-09-2021processing905177MEFMOV5AD9155B0C0DC-M2104.8104.80.0104.8Men's FashioncodGross1153222021Sep-21639345Ms.ShainaKNowakF56Nowak, Shainashaina.nowak@gmail.com29-11-2009320-11-4545215-735-9443ChambersburgChambersburgChambersburgPA17202Northeastsknowak0.0
28638210056236530-09-2021paid905179APPCHA5AF14939B8F8A24419.94419.90.04419.9AppliancesEasypayValid1153232021Sep-21967309Prof.BradyKLathamM51Latham, Bradybrady.latham@gmail.com21-03-2007613-87-0361212-772-7404RushvilleYatesRushvilleNY14544Northeastbklatham0.0
28638310056237630-09-2021cod905191MEFCOT5A8D1E973B886239.939.90.039.9Men's FashioncodValid1153242021Sep-21335358Prof.BennieMBrunettiM52Brunetti, Benniebennie.brunetti@gmail.com24-10-2011101-02-1040229-817-9451LawrencevilleGwinnettLawrencevilleGA30044Southbmbrunetti0.0
28638410056238330-09-2021cod905200WOFVAL59D5EA84167F9-M240.040.00.040.0Women's FashioncodValid1153252021Sep-21675384Mrs.FrancescaNGiustiF38Giusti, Francescafrancesca.giusti@btinternet.com25-07-1987399-31-7238252-414-8396DurhamDurhamDurhamNC27701Southfngiusti0.0
28638510056238430-09-2021cod905202WOFNIG5B4D7EB0E9FDD-L249.949.90.049.9Women's FashioncodValid1153252021Sep-21675384Mrs.FrancescaNGiustiF38Giusti, Francescafrancesca.giusti@btinternet.com25-07-1987399-31-7238252-414-8396DurhamDurhamDurhamNC27701Southfngiusti0.0
28638610056238630-09-2021processing905205MATHUA5AF70A7D1E50A23559.93559.90.03559.9Mobiles & TabletsbankalfalahGross1153262021Sep-21489455Mr.RolfESchlosserM28Schlosser, Rolfrolf.schlosser@comcast.net28-01-2015320-11-8748423-276-2699KnoxvilleKnoxKnoxvilleTN37920Southreschlosser0.0